Effects of Host Movement on Pathogen Population Structure and Epidemic Dynamics 公开
Bozick, Brooke (2016)
Abstract
This dissertation examines how host movement affects epidemic spread by characterizing spatial genetic patterns in pathogen populations. When hosts are highly mobile, measures of spatial distance should additionally incorporate the magnitude and frequency of movement between locations. Seasonal influenza A virus presents an ideal system with which to study the effects of mobility on viral dynamics due to extensive human transportation networks. To fully understand the processes of pathogen invasion and spread, a detailed understanding of the ecological and evolutionary factors that control distributional limits is necessary. I find that pathogens are not uniformly distributed across their hosts' ranges, and that pathogen evolutionary responses to conditions across the geographic range are modulated by both abiotic and biotic factors that differ across the landscape. An initial investigation of regional scale human mobility in the United States suggests that epidemics spread along predictable pathways defined by commuter volume. However, similar patterns are not detected for influenza epidemics in Europe. An analysis of the major European regional transportation networks reveals that both networks possess characteristics that facilitate long-distance transmission and international mixing of influenza. This analysis also uncovers important complexities associated with the spatial analysis of genetic sequence data, and a re-examination of US influenza epidemics leads to the conclusion that spatial structure based on mobility is not yet detectable in this system using the current genetic data. Finally, the effects of vaccination strategies targeted at different host age and social groups are evaluated using a stochastic metapopulation model simulating a city-suburb system. I find that targeting children provides the greatest benefits in terms of reducing incidence, but also show that vaccination of groups of employed adults provides similar reductions in incidence and additionally delays the speed and timing of inter-community spread when epidemics are severe and vaccine doses are limited. I conclude that the intricacies of epidemic spread make the detection of spatial genetic patterns based on movement networks difficult, but that the greater availability of high-resolution spatial genetic data will lead to a more detailed understanding of pathogen ecological and evolutionary dynamics.
Table of Contents
Chapter 1: Introduction
1.1 Linking host movement to pathogen population structure 1
1.2 Human movement and disease 2
Figure 1.1 Temporal and Spatial Scales Over Which Human 4 Movement Occurs and Implications for Pathogen Transmission
1.3 Influenza A virus as a model system 5
1.4 Dissertation summary 8
Chapter 2: Integrating parasites and pathogens into the study of geographic range limits
2.1 Introduction 11
2.2 Defining the geographic range 12
2.3 Range limits 13
2.4 Hosts as habitat 15
2.5 External environment 23
2.6 Community interactions 26
2.7 Using parasites to study range limits 29
2.8 Using parasites to understand host geographic range limits 30
2.9 Studying geographic ranges of parasites 31
2.10 Conclusions 32
2.11 Tables
Table 2.1 Factors affecting species' geographic range limits and their 36 application to parasites
2.12 Figures
Figure 2.1 Geographic distribution of rabies virus in the United States 37
Figure 2.2 The effect of patch connectivity on pathogen on global 38 pathogen persistence within a metapopulation system
Chapter 3: The role of human transportation networks in mediating the genetic structure of seasonal influenza in the United States
3.1 Introduction 39
3.2 Materials and Methods 42
3.3 Results 48
3.4 Discussion 50
3.5 Addendum 57
3.6 Tables
Table 3.1 H3N2 Mantel Correlation Coefficients 58
Table 3.2 H1N1 Mantel Correlation Coefficients 59
3.7 Figures
Figure 3.1 Aviation and Commuter Network Models for the 60 Continental US
Figure 3.2 US Commuting Communities 61
Chapter 4: Genetic structuring of seasonal influenza in Europe based on rail networks
4.1 Introduction 62
4.2 Materials and Methods 64
4.3 Results 69
4.4 Discussion 73
4.5 Figures
Figure 4.1 Geographic and Population Centroids of European 78 Countries
Figure 4.2 European Aviation and Rail Networks 79
Figure 4.7 Association Between Travel Volume and Geographic 80 Distance for the European Aviation and Rail Networks
Figure 4.4 Unweighted and Weighted Hub Indexes 81
Figure 4.5 High Volume Edges 82
Figure 4.6 US Commuting Network 83
Figure 4.7 Degree Distribution Comparison of Air and Ground 84 Transportation Networks
Figure 4.8 Regression of Nucleotide Diversity on Edge Volume: Europe 85
Figure 4.9 Genetic Diversity and Commuting Volume Within and 85 Between US States for the H1N1 and H3N2 Subtypes
Figure 4.10 Regression of H1N1 Nucleotide Diversity on Edge 86 Volume: US
Chapter 5: Modeling the effects of commuter-targeted vaccination strategies on influenza epidemics
5.1 Introduction 87
5.2 Methods 89
5.3 Results 95
5.4 Discussion 97
5.5 Figures
Figure 5.1 Nearest-Neighbor Commuting Network 100
Figure 5.2 Epidemic Timing and Incidence in Response to Targeted 101 Vaccination
Figure 5.3 Epidemic Timing Under Limited, Targeted Vaccination 102
Figure 5.4 Incidence Under Limited, Targeted Vaccination 103
Figure 5.5 Incidence by Age Group Under Limited, Targeted 104 Vaccination
Figure 5.6 Epidemic Timing Under Excess, Targeted Vaccination 105
Figure 5.7 Incidence Under Excess, Targeted Vaccination 106
Figure 5.8 Incidence by Age Group Under Excess, Targeted 107 Vaccination
Chapter 6: Summary and Conclusions
6.1 Effects of human mobility on pathogen evolutionary and ecological 109 dynamics
6.2 Conclusions, future directions and a call for data 111
Bibliography 113
Appendix I: Supplementary Material for Chapter 3
I.1 Supplementary Tables
Table S1 Accession numbers, locations and collection dates of all 147 sequences used in this study
Table S2 Number of sequences per season and number of locations 201 (US states) represented for influenza A/H3N2 and A/H1N1 by season
Table S3 Summary of epidemiological and evolutionary dynamics of 202 H3N2 epidemics based on phylogenetic analysis of each influenza season
Table S4 Summary of epidemiological and evolutionary dynamics of 204 H1N1 epidemics based on phylogenetic analysis of each influenza season
I.2 Supplementary Figures
Figure S1 2003-2004 H3N2 Phylogeny 206
Figure S2 2004-2005 H3N2 Phylogeny 207
Figure S3 2005-2006 H3N2 Phylogeny 208
Figure S4 2006-2007 H3N2 Phylogeny 209
Figure S5 2007-2008 H3N2 Phylogeny 210
Figure S6 2008-2009 H3N2 Phylogeny 211
Figure S7 2010-2011 H3N2 Phylogeny 212
Figure S8 2011-2012 H3N2 Phylogeny 213
Figure S9 2012-2013 H3N2 Phylogeny 214
Figure S10 2006-2007 H1N1 Phylogeny 215
Figure S11 2007-2008 H1N1 Phylogeny 216
Figure S12 2008-2009 H1N1 Phylogeny 217
Figure S13 2010-2011 H1N1 Phylogeny 218
Figure S14 2011-2012 H1N1 Phylogeny 219
Figure S15 2012-2013 H1N1 Phylogeny 220
Appendix II: Supplementary Material for Chapter 4
II.1 Supplementary Tables
Table S1 List of Accession Numbers 221
Table S2 European Country Codes 234
Table S3. Summary of Available Sequences 235
Table S4. Summary of Sequences Used 236
Table S5 Phylogenetic Analysis of European Influenza Epidemics 237
II.2 Supplementary Figures
Figure S1 2007-2008 H1N1 Phylogeny 239
Figure S2 2008-2009 H1N1 Phylogeny 240
Figure S3 2009-2010 H1N1 Phylogeny 241
Figure S4 2010-2011 H1N1 Phylogeny 242
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